A multi-aspect agent-based model of covid-19: disease dynamics, contact tracing interventions and shared space-driven contagions

Abstract

In the quest to better understand the epidemic dynamics of COVID-19 and possible strategies to mitigate its impact, a wide range of simulation models have been developed for various purposes. Faced with a novel disease with little-known characteristics and an unprecedented impact, the need arises to model multiple aspects with very dissimilar dynamics in a consistent, formal, yet flexible and quick way, in order to then study the combined interaction of these dynamics. We present an agent-based model combining kinematic movement of agents, interaction between them and their surrounding space and a top-down control over the entire population. To achieve this, we extend the retQSS framework to model and simulate particle systems interacting with geometries. In this work, we study different contact tracing strategies and their efficacy in a population undergoing an epidemic process driven mainly by airborne infections in indoor environments.